Load popular digits dataset from sklearn.datasets module and assign it to variable digits.
Split digits.data into two sets names X_train and X_test. Also, split digits.target into two sets Y_train and Y_test.
Hint: Use train_test_split() method from sklearn.model_selection; set random_state to 30; and perform stratified sampling. Build an SVM classifier from X_train set and Y_train labels, with default parameters. Name the model as svm_clf.
Evaluate the model accuracy on the testing data set and print its score. I used the following code:
import sklearn.datasets as datasets import sklearn.model_selection as ms from sklearn.model_selection import train_test_split digits = datasets.load_digits(); X = digits.data y = digits.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30) print(X_train.shape) print(X_test.shape) from sklearn.svm import SVC svm_clf = SVC().fit(X_train, y_train) print(svm_clf.score(X_test,y_test)) I got the below output.
(1347,64) (450,64) 0.4088888888888889 But I am not able to pass the test. Can someone help with what is wrong?
